P.062 Fun for the brain: activities promoting stroke recovery in the acute phase
Bibliographic record
Abstract
Background: Canadian Stroke Best Practices recommend early mobilization and engagement in activities after stroke to enhance recovery. StrokeEngine reports the use of music can further promote recovery by harnessing neuroplasticity. Using music to enhance participation in activities after stroke may impact favorably on outcome after stroke. Methods: This descriptive study will be offered to patients admitted on the stroke unit. Based on the music preferences of willing participants and guided by the physiotherapy assessment, music, singing or dance movements will be incorporated into extra-therapeutic activities using specific musical instruments matched to patient ability. The music-enhanced activity program includes at least 3 sessions per week with a trained volunteer and additional sessions with family members for the duration of the hospital stay. Each session will last between 20 and 30 mins. The program will run for six weeks. Results: Data on patient participation in daily therapy and activities on the stroke unit will be presented and compared to a similar group of stroke patients. Changes in patient stroke recovery parameters will be measured and reported on magnitude of change for future work. Conclusions: Innovative ways to enhance patient engagement early after a stroke can optimalize stroke recovery. This project will shed some light on the effects of a music-enhanced intervention
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.151 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".